Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image.
Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as...
| Publicado en: | BioMed Research International pp. 1 - 13 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/2/2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158307738&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158307738 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/2/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158307738 158307738 158307738 10.1155/2022/6451770 158307738 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image. aug: au: Vadivu, N. Shanmuga Gupta, Gauri Naveed, Quadri Noorulhasan Rasheed, Tariq Singh, Sitesh Kumar Dhabliya, Dharmesh affil: Department of Electronics and Communications Engineering, RVS College of Engineering and Technology, Coimbatore, India sug: subj: Lung Neoplasms Diagnosis Tomography, X-Ray Computed Health Information Early Diagnosis Lung Neoplasms Prognosis Human Radiography, Thoracic Radiography, Abdominal Positron-Emission Tomography ab: Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|